Micro-cracks are often generated on the concrete structures of long-distance water conveyance projects. Without early detection and timely maintenance, micro-cracks may expand and deteriorate continuously, leading to major structural failure and disastrous results. However, due to the complexity of the underwater environment, many vision-based methods for concrete crack detection cannot be directly applied to the interior surface of water conveyance structures. In view of this, this paper proposes a three-step method to automatically detect concrete micro-cracks of underwater structures during the operation period. First, underwater optical images were preprocessed by a series of algorithms such as global illumination balance, image color correction, and detail enhancement. Second, the preprocessed images were sliced to image patches, which are sent to a convolutional neural network for crack recognition and crack boundary localization. Finally, the image patches containing cracks were segmented by the Otsu algorithm to localize the cracks precisely. The proposed method can overcome issues such as uneven illumination, color distortion, and detail blurring, and can effectively detect and localize cracks in underwater optical images with low illumination, low signal-to-noise ratio and low contrast. The experimental results show that this method can achieve a true positive rate of 93.9% for crack classification, and the identification accuracy of the crack width can reach 0.2 mm.

长距离输水工程的混凝土结构上经常会产生微裂缝。如果不及早发现和及时维护，微裂纹可能会不断扩大和恶化，导致重大结构故障和灾难性后果。然而，由于水下环境的复杂性，许多基于视觉的混凝土裂缝检测方法不能直接应用于输水结构的内表面。有鉴于此，本文提出了一种三步法在作业期间自动检测水下结构混凝土微裂缝。首先，通过全局光照平衡、图像色彩校正、细节增强等一系列算法对水下光学图像进行预处理。其次，将预处理后的图像切成图像块，它们被发送到卷积神经网络进行裂纹识别和裂纹边界定位。最后，通过 Otsu 算法对包含裂缝的图像块进行分割，以精确定位裂缝。该方法可以克服光照不均匀、颜色失真、细节模糊等问题，能够有效检测和定位低光照、低信噪比和低对比度的水下光学图像中的裂缝。实验结果表明，该方法对裂纹分类的真阳性率可达93.9%，裂纹宽度识别精度可达0.2 mm。包含裂缝的图像块通过 Otsu 算法进行分割，以精确定位裂缝。该方法可以克服光照不均匀、颜色失真、细节模糊等问题，能够有效检测和定位低光照、低信噪比和低对比度的水下光学图像中的裂缝。实验结果表明，该方法对裂纹分类的真阳性率可达93.9%，裂纹宽度识别精度可达0.2 mm。包含裂缝的图像块通过 Otsu 算法进行分割，以精确定位裂缝。该方法可以克服光照不均匀、颜色失真、细节模糊等问题，能够有效检测和定位低光照、低信噪比和低对比度的水下光学图像中的裂缝。实验结果表明，该方法对裂纹分类的真阳性率可达93.9%，裂纹宽度识别精度可达0.2 mm。